Markov Decision Processes (MDPs) provide a mathematical framework for modeling decision-making in situations where outcomes are partly random and partly under the control of a decision maker. They are used to find optimal policies by balancing immediate rewards with future benefits, leveraging the Markov property which assumes that the future state depends only on the current state and action, not on the sequence of events that preceded it.